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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.01743">arXiv:2503.01743</a> <span> [<a href="https://arxiv.org/pdf/2503.01743">pdf</a>, <a href="https://arxiv.org/format/2503.01743">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Microsoft"> Microsoft</a>, <a href="/search/cs?searchtype=author&query=%3A"> :</a>, <a href="/search/cs?searchtype=author&query=Abouelenin%2C+A">Abdelrahman Abouelenin</a>, <a href="/search/cs?searchtype=author&query=Ashfaq%2C+A">Atabak Ashfaq</a>, <a href="/search/cs?searchtype=author&query=Atkinson%2C+A">Adam Atkinson</a>, <a href="/search/cs?searchtype=author&query=Awadalla%2C+H">Hany Awadalla</a>, <a href="/search/cs?searchtype=author&query=Bach%2C+N">Nguyen Bach</a>, <a href="/search/cs?searchtype=author&query=Bao%2C+J">Jianmin Bao</a>, <a href="/search/cs?searchtype=author&query=Benhaim%2C+A">Alon Benhaim</a>, <a href="/search/cs?searchtype=author&query=Cai%2C+M">Martin Cai</a>, <a href="/search/cs?searchtype=author&query=Chaudhary%2C+V">Vishrav Chaudhary</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+C">Congcong Chen</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+D">Dong Chen</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+D">Dongdong Chen</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+J">Junkun Chen</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Weizhu Chen</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yen-Chun Chen</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+Y">Yi-ling Chen</a>, <a href="/search/cs?searchtype=author&query=Dai%2C+Q">Qi Dai</a>, <a href="/search/cs?searchtype=author&query=Dai%2C+X">Xiyang Dai</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+R">Ruchao Fan</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+M">Mei Gao</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+M">Min Gao</a>, <a href="/search/cs?searchtype=author&query=Garg%2C+A">Amit Garg</a>, <a href="/search/cs?searchtype=author&query=Goswami%2C+A">Abhishek Goswami</a> , et al. (51 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.01743v2-abstract-short" style="display: inline;"> We introduce Phi-4-Mini and Phi-4-Multimodal, compact yet highly capable language and multimodal models. Phi-4-Mini is a 3.8-billion-parameter language model trained on high-quality web and synthetic data, significantly outperforming recent open-source models of similar size and matching the performance of models twice its size on math and coding tasks requiring complex reasoning. This achievement… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.01743v2-abstract-full').style.display = 'inline'; document.getElementById('2503.01743v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.01743v2-abstract-full" style="display: none;"> We introduce Phi-4-Mini and Phi-4-Multimodal, compact yet highly capable language and multimodal models. Phi-4-Mini is a 3.8-billion-parameter language model trained on high-quality web and synthetic data, significantly outperforming recent open-source models of similar size and matching the performance of models twice its size on math and coding tasks requiring complex reasoning. This achievement is driven by a carefully curated synthetic data recipe emphasizing high-quality math and coding datasets. Compared to its predecessor, Phi-3.5-Mini, Phi-4-Mini features an expanded vocabulary size of 200K tokens to better support multilingual applications, as well as group query attention for more efficient long-sequence generation. Phi-4-Multimodal is a multimodal model that integrates text, vision, and speech/audio input modalities into a single model. Its novel modality extension approach leverages LoRA adapters and modality-specific routers to allow multiple inference modes combining various modalities without interference. For example, it now ranks first in the OpenASR leaderboard to date, although the LoRA component of the speech/audio modality has just 460 million parameters. Phi-4-Multimodal supports scenarios involving (vision + language), (vision + speech), and (speech/audio) inputs, outperforming larger vision-language and speech-language models on a wide range of tasks. Additionally, we experiment to further train Phi-4-Mini to enhance its reasoning capabilities. Despite its compact 3.8-billion-parameter size, this experimental version achieves reasoning performance on par with or surpassing significantly larger models, including DeepSeek-R1-Distill-Qwen-7B and DeepSeek-R1-Distill-Llama-8B. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.01743v2-abstract-full').style.display = 'none'; document.getElementById('2503.01743v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">39 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2003.01680">arXiv:2003.01680</a> <span> [<a href="https://arxiv.org/pdf/2003.01680">pdf</a>, <a href="https://arxiv.org/format/2003.01680">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shalyminov%2C+I">Igor Shalyminov</a>, <a href="/search/cs?searchtype=author&query=Sordoni%2C+A">Alessandro Sordoni</a>, <a href="/search/cs?searchtype=author&query=Atkinson%2C+A">Adam Atkinson</a>, <a href="/search/cs?searchtype=author&query=Schulz%2C+H">Hannes Schulz</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2003.01680v2-abstract-short" style="display: inline;"> Domain adaptation has recently become a key problem in dialogue systems research. Deep learning, while being the preferred technique for modeling such systems, works best given massive training data. However, in the real-world scenario, such resources aren't available for every new domain, so the ability to train with a few dialogue examples can be considered essential. Pre-training on large data… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.01680v2-abstract-full').style.display = 'inline'; document.getElementById('2003.01680v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2003.01680v2-abstract-full" style="display: none;"> Domain adaptation has recently become a key problem in dialogue systems research. Deep learning, while being the preferred technique for modeling such systems, works best given massive training data. However, in the real-world scenario, such resources aren't available for every new domain, so the ability to train with a few dialogue examples can be considered essential. Pre-training on large data sources and adapting to the target data has become the standard method for few-shot problems within the deep learning framework. In this paper, we present the winning entry at the fast domain adaptation task of DSTC8, a hybrid generative-retrieval model based on GPT-2 fine-tuned to the multi-domain MetaLWOz dataset. Robust and diverse in response generation, our model uses retrieval logic as a fallback, being SoTA on MetaLWOz in human evaluation (>4% improvement over the 2nd place system) and attaining competitive generalization performance in adaptation to the unseen MultiWOZ dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2003.01680v2-abstract-full').style.display = 'none'; document.getElementById('2003.01680v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 March, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Presented at DSTC8@AAAI 2020</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1911.06394">arXiv:1911.06394</a> <span> [<a href="https://arxiv.org/pdf/1911.06394">pdf</a>, <a href="https://arxiv.org/format/1911.06394">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> The Eighth Dialog System Technology Challenge </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+S">Seokhwan Kim</a>, <a href="/search/cs?searchtype=author&query=Galley%2C+M">Michel Galley</a>, <a href="/search/cs?searchtype=author&query=Gunasekara%2C+C">Chulaka Gunasekara</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+S">Sungjin Lee</a>, <a href="/search/cs?searchtype=author&query=Atkinson%2C+A">Adam Atkinson</a>, <a href="/search/cs?searchtype=author&query=Peng%2C+B">Baolin Peng</a>, <a href="/search/cs?searchtype=author&query=Schulz%2C+H">Hannes Schulz</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+J">Jianfeng Gao</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Jinchao Li</a>, <a href="/search/cs?searchtype=author&query=Adada%2C+M">Mahmoud Adada</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+M">Minlie Huang</a>, <a href="/search/cs?searchtype=author&query=Lastras%2C+L">Luis Lastras</a>, <a href="/search/cs?searchtype=author&query=Kummerfeld%2C+J+K">Jonathan K. Kummerfeld</a>, <a href="/search/cs?searchtype=author&query=Lasecki%2C+W+S">Walter S. Lasecki</a>, <a href="/search/cs?searchtype=author&query=Hori%2C+C">Chiori Hori</a>, <a href="/search/cs?searchtype=author&query=Cherian%2C+A">Anoop Cherian</a>, <a href="/search/cs?searchtype=author&query=Marks%2C+T+K">Tim K. Marks</a>, <a href="/search/cs?searchtype=author&query=Rastogi%2C+A">Abhinav Rastogi</a>, <a href="/search/cs?searchtype=author&query=Zang%2C+X">Xiaoxue Zang</a>, <a href="/search/cs?searchtype=author&query=Sunkara%2C+S">Srinivas Sunkara</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+R">Raghav Gupta</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1911.06394v1-abstract-short" style="display: inline;"> This paper introduces the Eighth Dialog System Technology Challenge. In line with recent challenges, the eighth edition focuses on applying end-to-end dialog technologies in a pragmatic way for multi-domain task-completion, noetic response selection, audio visual scene-aware dialog, and schema-guided dialog state tracking tasks. This paper describes the task definition, provided datasets, and eval… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.06394v1-abstract-full').style.display = 'inline'; document.getElementById('1911.06394v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1911.06394v1-abstract-full" style="display: none;"> This paper introduces the Eighth Dialog System Technology Challenge. In line with recent challenges, the eighth edition focuses on applying end-to-end dialog technologies in a pragmatic way for multi-domain task-completion, noetic response selection, audio visual scene-aware dialog, and schema-guided dialog state tracking tasks. This paper describes the task definition, provided datasets, and evaluation set-up for each track. We also summarize the results of the submitted systems to highlight the overall trends of the state-of-the-art technologies for the tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1911.06394v1-abstract-full').style.display = 'none'; document.getElementById('1911.06394v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 November, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to NeurIPS 2019 3rd Conversational AI Workshop</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1710.07300">arXiv:1710.07300</a> <span> [<a href="https://arxiv.org/pdf/1710.07300">pdf</a>, <a href="https://arxiv.org/format/1710.07300">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> FigureQA: An Annotated Figure Dataset for Visual Reasoning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kahou%2C+S+E">Samira Ebrahimi Kahou</a>, <a href="/search/cs?searchtype=author&query=Michalski%2C+V">Vincent Michalski</a>, <a href="/search/cs?searchtype=author&query=Atkinson%2C+A">Adam Atkinson</a>, <a href="/search/cs?searchtype=author&query=Kadar%2C+A">Akos Kadar</a>, <a href="/search/cs?searchtype=author&query=Trischler%2C+A">Adam Trischler</a>, <a href="/search/cs?searchtype=author&query=Bengio%2C+Y">Yoshua Bengio</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1710.07300v2-abstract-short" style="display: inline;"> We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts. We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.07300v2-abstract-full').style.display = 'inline'; document.getElementById('1710.07300v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1710.07300v2-abstract-full" style="display: none;"> We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts. We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection. To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure. To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives. In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements. We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as a strong baseline. Preliminary results indicate that the task poses a significant machine learning challenge. We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1710.07300v2-abstract-full').style.display = 'none'; document.getElementById('1710.07300v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 February, 2018; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 October, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">workshop paper at ICLR 2018</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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